code: def _predict_rho(self, observation):
      extracted_obs = process(observation)
      obs = torch.tensor(extracted_obs, dtype=torch.float32).view(1, -1)
      predicted_rho = (
          self.powernet_model(obs).detach().cpu().numpy().reshape(-1)
      )
      sorted_idx = np.argsort(predicted_rho)
      return sorted_idx.tolist(), predicted_rho
  
file_path: PARL/benchmark/torch/NeurIPS2020-Learning-to-Run-a-Power-Network-Challenge/track2/rl_agent.py
imports: import os
  import numpy
  from copy import deepcopy
  from utils import process
  from powernet_model import PowerNetModel
  from grid2op.Agent import BaseAgent
  import torch
label_desc: Use image node_id 6 for calling the RLAgent obj's underlying member method code with example usage: obj._predict_rho(observation) and returns: sorted_idx,  predicted_rho